Results 141 to 150 of about 20,022 (282)

Maximizing submodular functions using probabilistic graphical models [PDF]

, 2013
We consider the problem of maximizing submodular functions; while this problem is known to be NP-hard, several numerically efficient local search techniques with approximation guarantees are available.
Sesh Kumar, K. S., Bach, Francis, K. S. Sesh Kumar, Francis Bach   +3 more
core  

LLM‐Integrated Human–Robot Interaction System for Microrobots

Advanced Robotics Research, EarlyView.
This paper proposes an LLM‐based control framework for guiding microrobots using human natural language. This framework can convert the natural human speech into safe and executable command sets for reliable navigation in complex environments. The experimental results show high accuracy and robustness in task performance, demonstrating the potential of
Bairong Zhu, Amar Salehi, Tingting Yu
wiley   +1 more source

Approximate Implication for Probabilistic Graphical Models

Journal of Artificial Intelligence Research
The graphical structure of Probabilistic Graphical Models (PGMs) represents the conditional independence (CI) relations that hold in the modeled distribution. Every separator in the graph represents a conditional independence relation in the distribution, making them the vehicle through which new conditional independence relations are inferred and ...
openaire   +2 more sources

Intelligent Maintenance Review for Robots: Multimodal Information, Deep Diagnosis and Embodied Artificial Intelligence

Advanced Robotics Research, EarlyView.
This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao, Hongbo Wang, Naipeng Li, Shuo Wang, Yaguo Lei, Tonghai Wu, Junyi Cao   +6 more
wiley   +1 more source

Functional connectomics from neural dynamics: probabilistic graphical models for neuronal network of Caenorhabditis elegans. [PDF]

Philos Trans R Soc Lond B Biol Sci, 2018
Liu H, Kim J, Shlizerman E.
europepmc   +1 more source

Learning‐Based Soft Robotic Grasping: Recent Progress and Remaining Challenges

Advanced Robotics Research, EarlyView.
This review analyzes learning‐based soft robotic grasping from a pipeline‐oriented perspective, encompassing soft gripper design, multimodal sensing, and learning‐based planning and control. It surveys key neural network architectures and benchmark datasets and identifies critical challenges such as sim‐to‐real transfer, generalization, and continual ...
Arnab Majumder, Jelizaveta Konstantinova, Graham Deacon, Kaspar Althoefer   +3 more
wiley   +1 more source

Probabilistic graphical models relate immune status with response to neoadjuvant chemotherapy in breast cancer. [PDF]

Oncotarget, 2018
Zapater-Moros A, Gámez-Pozo A, Prado-Vázquez G, Trilla-Fuertes L, Arevalillo JM, Díaz-Almirón M, Navarro H, Maín P, Feliú J, Zamora P, Espinosa E, Fresno Vara JÁ.   +11 more
europepmc   +1 more source

In Situ X‐Ray Tomography and Acoustic Emission Monitoring of Damage Evolution in C/C‐SiC Composites Fabricated by Liquid Silicon Infiltration

Advanced Science, EarlyView.
This study investigates how the internal structure of fiber‐reinforced ceramic composites affects their resistance to damage. By combining 3D X‐ray imaging with acoustic emission monitoring during mechanical testing, it reveals how silicon distribution influences crack formation.
Yang Chen, Renato S. M. Almeida, Stefan Flauder, Guillaume Couégnat, Kamen Tushtev, Jürgen Horvath, Peter Wriggers, Kurosch Rezwan   +7 more
wiley   +1 more source

Dissecting the Ecological Structure of Health and Disease in the Global Gut Microbiome

Advanced Science, EarlyView.
We introduce Wiredancer, a framework that identifies three continuous ecological factors of the gut microbiota. These factors exhibit distinct patterns across health and disease, jointly capturing disrupted ecological stability and offering a new perspective for precision diagnostics and therapeutic strategies.
Baoyuan Zhu, Shuhao Chen, Yunheng Diao, Wei Wang, Yuanyuan Huang, Liqin Liang, Xiaodan Lu, Rui Han, Minxin Guo, Zhaobo Li, Shuhong Wang, Hehua Li, Chenyu Liu, Jing Zhou, Dongsheng Xiong, Xiaobo Li, Yuping Ning, Xuetao Shi, Fengchun Wu, Kai Wu   +19 more
wiley   +1 more source

Probabilistic Graphical Models Follow Directly From Maximum Entropy

, 2017
Probabilistic graphical models are a very efficient machine learning technique. However, their only known justification is based on heuristic ideas, ideas that do not explain why exactly these models are empirically successful.
Zapata, Francisco, Ly, Anh H., Kreinovich, Vladik, Fuentes, Olac   +3 more
core   +1 more source